1. Introduction
The current energy outlook points to a need for change in the way energy is produced. According to the U.S. Energy Information Administration, global energy consumption will increase by 34% between 2022 and 2050 [1]. During the same period, energy-related CO2 emissions will increase by 15% [1]. In this sense, current research focuses on the development of renewable and clean energy sources. In this context, hydrogen (H2) is considered a clean energy source because the only by-product of its combustion is water [2]. In addition, H2 has a relatively high energy content (142 kJ/g) compared to most fossil fuels, making it a promising alternative fuel [3].
Currently, H2 is primarily produced from fossil fuels, particularly methane, and is widely used in the chemical fertilizer and petroleum industries [4]. However, using fossil fuels for H2 production poses similar economic and environmental challenges as other fossil fuel-based processes. Alternatively, H2 can be produced through water electrolysis, which is considered the cleanest method of production. Despite its environmental benefits, the high electricity costs associated with electrolysis—accounting for about 80% of total operating expenses—limit its practical application [5]. In contrast, biological processes for H2 production from biomass (biogenic H2) offer an environmentally friendly and energy-efficient alternative [6]. Among these biological methods, dark fermentation (DF) is the most extensively researched due to its high production rates, compatibility with anaerobic digestion, and ability to process a wide range of organic waste and wastewater sources.
Many types of agro-industrial residues, such as wheat straw, sugarcane bagasse, corn stover, etc., have been studied as feedstocks for H2 production by DF [7]. Rice is the second most cultivated cereal crop in the world after wheat. According to the Food and Agriculture Organization of the United Nations, 787.3 million tons of rice were produced in 2021 [8], resulting in approximately 800 to 1000 million tons of rice straw (RS) as residue [9]. Only 20% of the RS produced worldwide is utilized, while the remaining 80% is discarded as waste. In many countries, the common practice for RS disposal is open burning, which contributes to air pollution [10]. RS can be a suitable feedstock for fermentative H2 production due to its widespread availability and favorable quality [11]. Therefore, numerous studies have focused on the utilization of this resource for H2 production. For example, Chen et al. [12] investigated the effect of temperature (ranging from 37 to 55 °C) and total solids content (3%, 6%, and 12%) on the performance of a dark fermenter operated under a semi-continuous regime. They achieved the highest H2 yield, 63.6 ± 2.9 mL H2/g vs. added, at a total solids content of 6% and a temperature of 55 °C. Also, Cai et al. [13] evaluated the performance of a DF process run on RS previously pretreated by NaOH/urea and electro-hydrolysis, obtaining productions of 163.0 mL H2/g RS.
Currently, the use of metal nanoparticles (NPs) to enhance H2 production in the DF process has become an attractive topic for numerous research groups. It is widely recognized that NPs can play a role in the DF process by influencing its mechanism in various ways, including improving electron transfer efficiency, enhancing feedstock utilization, enhancing metalloenzyme activity, and immobilizing enzymes, among others [14]. Compared with other metallic NPs, iron and nickel NPs have shown greater potential for their application in DF [14]. In particular, iron-based nanoparticles (FeNPs) play a pivotal role in enhancing the activity of metalloenzymes, including both types of hydrogenases (Fe-Fe and Fe-Ni). In addition, FeNPs show a tendency to optimize the utilization of raw materials, resulting in improved H2 production [15]. Recent research efforts suggest that conductive iron oxides, such as Fe3O4 (magnetite), play an important role in facilitating electron transfer between ferredoxin oxidoreductase and hydrogenases secreted by syntrophic bacteria. This enhancement promotes the anaerobic degradation of fermentable carbohydrates and facilitates the reduction of protons to H2 [16,17].
Research on the use of NPs to enhance fermentative H2 production has been predominantly limited to batch operations [4,18,19]. This has impeded a comprehensive investigation into the influence of NPs in continuous reactor systems. In this context, the research conducted by Tawfik et al. [16] is noteworthy because it was the first study to evaluate the role of Fe3O4 NPs in improving the continuous production of H2. Such a study evaluated the performance of two anaerobic baffled reactors using alkaline hydrolysates of RS. The inclusion of magnetite NPs resulted in a significant improvement in the H2 production yield. For example, the maximum H2 yield reached 21.4 ± 3.2 mL/g COD removed for reactor 1 and 60.6 ± 5.7 mL/g COD removed for reactor 2, both at an organic loading rate (OLR) of 1.6 g COD/L-d. Despite the promising results, the study was conducted at a relatively low OLR, with a maximum of 4.8 g COD/L-d, which resulted in a modest H₂ productivity of approximately 200 mL H₂/L-d. Furthermore, the effect of varying nanoparticle concentrations on productivity and system performance was not explored, as the evaluation was limited to the addition of nanoparticles solely during the inoculation step. This restricted the ability to assess how the continuous addition of magnetite nanoparticles might influence H₂ production and overall reactor efficiency.
Therefore, the aim of this study was to evaluate the effect of continuous supplementation of FeNPs in a DF system for H2 production using hydrolysates of thermally dilute acid-pretreated rice straw. In addition, the impact of FeNP addition on microbial community composition and organic acid production was investigated, providing a more comprehensive understanding of the overall process.
2. Materials and Methods
2.1. Inoculum
The anaerobic fermentation reactor was inoculated with an acidogenic inoculum derived from the digestate of a mesophilic pilot-scale anaerobic digester processing food waste [20]. To inhibit methanogenic communities, the digestate underwent a heat shock pretreatment at 90 °C for 20 min. The resulting mixed culture was then enriched through successive subcultures using lactose as the sole carbon source (10 g/L) and a growth medium containing the following components per liter: NH4Cl 2.4 g, K2HPO4 2.4 g, MgCl2⋅6H2O 2.5 g, KH2PO4 0.6 g, CaCl2⋅2H2O 0.15 g, and FeCl2⋅4H2O 0.035 g [21].
2.2. Rice Straw Hydrolysate
RS was kindly provided by a rice mill located in Valencia, Spain. The residue was dried at ambient temperature. The RS was ground to a reduced size in the range of 2 to 3 mm. For preservation, the RS was packaged in bundles of approximately 6 kg each and stored in a moisture-free room until use. The material was subjected to physicochemical characterization (see Table 1) according to the National Renewable Energy Laboratory (NREL) procedures [22].
RS underwent dilute acid-assisted thermal pretreatment according to the procedure described by Castilla-Archilla et al. [23]. A total solids concentration of 7% w/w RS was used for the hydrolysis process. Sulfuric acid (96% purity and density of 1.84 g/mL) supplied by AppliChem (Darmstadt, Germany) was used at a concentration of 1.5% v/v to perform the acid hydrolysis, which was carried out in a 10 L borosilicate bottle with a working volume of 5 L. The homogenized solution was then autoclaved (Raypa, Terrassa, Spain) at 121 °C for 20 min. At the end of the hydrolysis, the borosilicate bottle was cooled to stop the reaction, and the resulting hydrolysate was filtered through a #20 sieve (850 µm) to recover the liquid fraction, which was stored in a sterile plastic bottle at 4 °C until needed. The physicochemical characterization of the hydrolysate is shown in Table 2.
2.3. Setup and Operating Parameters
Figure 1 illustrates the reactor setup used for the continuous H2 production system, which consisted of a polyvinyl chloride fermenter with a total capacity of 1.2 L with a 0.4 L headspace. The setup was equipped with a pH controller (BSV, Spain) and a pH probe (HO35-BSV01, Spain), along with gas and liquid phase sampling ports. The system was also equipped with an integrated continuous gas flow meter (see Figure 1). Prior to introducing the inoculum into the reactor, a 0.1 L portion of the acidogenic inoculum was activated in a 2.1 L gas-tight fermenter (1 L working volume) for 24 h at 37 ± 1 °C, maintained at approximately 150 rpm. During this activation period, pH control was not applied, and the inoculum was cultured in mineral growth medium supplemented with 10 g/L lactose as described in Section 2.1. The reactor was initially inoculated with 10% v/v of fresh, actively cultured inoculum and operated in batch mode for approximately 8 h before transitioning to continuous operation. Lactose and mineral media were added solely during this initial batch phase to support microbial activity.
The continuous operation included a start-up and stabilization period, followed by four operating conditions. The first condition assessed the system without the addition of nanoparticles (NPs) at an HRT of 6 h, corresponding to an OLR of 100.4 g COD/L-day, after which increasing concentrations of FeNPs (iron (II, III) oxide nanopowder, Sigma, USA) were introduced at 50, 100, and 200 mg/L. According to the specifications provided by the supplier, the nanopowder used had a particle size distribution ranging from 50 to 100 nm.
During the start-up phase, the hydrolysate was prepared for operation. Briefly, the reactor was inoculated as described above and operated on a 24 h HRT. Once the reactor reached a stable production state, the HRT was reduced to 12 h, resulting in system inhibition after two days of operation. In response, the reactor was run in batch mode for two days with lactose added for recovery. Due to the poor performance of the reactor, bioaugmentation was required. A significant improvement in reactor productivity within 8 h allowed a switch to continuous mode (HRT 12 h), which was maintained for two days while still feeding lactose.
Once production stability was established, there was a shift to using RS hydrolysates as the substrate, with an increased HRT of 18 h. However, after 8 days of operation, the reactor activity decreased, eventually leading to total inhibition. Consequently, a new start-up strategy was implemented.
To reduce the presence and accumulation of inhibitors, the hydrolysates were subjected to the following over-liming treatment: Calcium hydroxide (Ca(OH)2) was added to the hydrolysates heated at 50 °C and stirred continuously until a pH of 10 was reached, and the mixture was allowed to stand for 30 min. The supernatant was then filtered and cooled to 30 °C for subsequent neutralization to pH 6 with 1N sulfuric acid (H2SO4). Activated carbon was added at a concentration of 1.5% w/v and mixed for 1 h, followed by further filtration. Finally, before use, the hydrolysates were supplemented with nutrients based on the composition of the growth medium. A settling phenomenon of the nutrients was observed when the activated carbon stage was introduced; therefore, its use was discontinued.
Throughout the operation, the reactor was maintained at 37 ± 1 °C in a temperature-controlled chamber. Magnetic stirring was applied at approximately 300 rpm, and the pH was maintained at 6.0 ± 0.1 using 6 M NaOH. Periodic samples were collected from both the influent and effluent to evaluate the organic acid profile and the effectiveness of total carbohydrate and COD removal. In addition, the flow rate and composition of the gas generated during the DF process were measured and recorded as standard volumes (in NmL or NL) under standard temperature and pressure conditions (0 °C and 1 atm).
2.4. Analytical Methods and Data Interpretation
Sugar composition (including glucose, xylose, and arabinose), inhibitor concentrations (furfural and 5-hydroxymethylfurfural, HMF), and organic acids were analyzed by high-performance liquid chromatography (HPLC) on a Shimadzu HPLC system (LC-2050C, Shimadzu Europa Analytical Instruments, Kyoto, Japan) equipped with a refractive index detector (RID-20A, Shimadzu RID 20A, Kyoto, Japan) coupled to a UV detector (214 nm). For the analysis of sugars, a Rezex chromatographic column (RHM-Monosaccharide H+, Phenomenex, Aschaffenburg, Germany) was used at a column temperature of 60 °C and a flow rate of 0.6 mL/min of eluent (5 mM H2SO4).
For the analysis of organic acids and inhibitors, an Aminex HPX-87H chromatographic column (Bio Rad, Hercules, CA, USA) was used, with the column temperature maintained at 75 °C and the eluent consisting of 25 mM H2SO4 at a flow rate of 0.7 mL/min. The H2 content of the gas was determined using gas chromatography with an Agilent GC system (8860-GC, Agilent, Wilmington, DE, USA) equipped with a thermal conductivity detector and a packed column (28.0 × 30.5 × 16.5 cm). This analytical method was also capable of detecting and quantifying carbon dioxide (CO2), methane (CH4), and hydrogen sulfide (H2S), if present.
Total organic carbon (TOC) and total nitrogen (TN) were determined using a Shimadzu total organic carbon analyzer (TOC-Vcsh, Shimadzu, Kyoto, Japan). Total solids (TS), volatile solids (VS), and COD were determined according to standard methods [24]. Total and reducing sugars were quantified using the phenol-sulfur method and the dinitro-salicylic acid (DNS) method, respectively [25,26].
The microbial community structure was analyzed during the steady state of the fermentation reactor. DNA extraction was carried out using the FastDNATM SPIN Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer’s protocol. The extracted DNA samples were sent to Novogene (Cambridge, UK) for 16S rRNA gene amplicon sequencing. Specifically, the V4-V5 region of the 16S rRNA gene was amplified by PCR using the primer pair 515F/907R. PCR products of the correct size were selected via agarose gel electrophoresis. Equal volumes of these PCR products from each sample were pooled, end-repaired, A-tailed, and ligated with Illumina adapters. Library quantification was performed using Qubit and real-time PCR, and the size distribution was assessed using a bioanalyzer. The quantified libraries were then pooled and sequenced on an Illumina paired-end platform. Amplicon Sequence Variants (ASVs) were generated using the DADA2 pipeline implemented within the QIIME 2 software (Version 202202) package and annotated with the Silva 138.1 database.
Finally, a comparative analysis of H2 productivity at different concentrations of FeNPs was performed using a one-way ANOVA conducted with STATGRAPHICS Centurion XVI, version 16.1.03.
3. Results and Discussion
3.1. Physicochemical Properties of Raw RS Material and Acid Hydrolysates
The proportions of total lignin, cellulose, and hemicellulose (by mass) in the raw RS were measured at 25.0 ± 0.2%, 25.4 ± 0.2%, and 7.8 ± 0.1%, respectively (Table 1). The results for cellulose and lignin in this study are consistent with previous research characterizing RS, falling within the typical ranges of 28–41% for cellulose and 5–24% for lignin [27,28]. However, the hemicellulose content observed in this study was lower than the values typically reported in the literature, which range from 15% to 30% [27,28]. This discrepancy may be due to a potential underestimation of the hemicellulose fraction, as it was determined based solely on xylose quantification, excluding other major pentoses such as arabinose. Additionally, methodological limitations could have contributed to incomplete hemicellulose hydrolysis [29]. Consequently, other pentoses might account for approximately 6.3% of the total dry matter composition [30].
Table 2 provides details on the characterization of the acid hydrolysates, with organic matter content measured as total and soluble COD (TCOD and SCOD, respectively), recorded at 33.7 ± 1.3 g/L and 28.5 ± 0.9 g/L, respectively. The COD yield, determined by the ratio of TCOD in hydrolysates to the total solids (TS) concentration of 7% (w/w), was 48.2%. The observed TCOD value is lower than that reported by Chang et al. [28], likely due to the higher TS concentration of 30% (w/w) used in their acid-assisted pretreatment study. The carbon-to-nitrogen ratio was found to be 22, which is within the ideal range of 20 to 30 for supporting anaerobic bacterial growth [31]. This indicates that RS can be effectively used as the sole substrate in anaerobic fermentation.
3.2. Dark Fermentation Reactor Performance Evaluation
3.2.1. Startup Strategy and Reactor Stabilization Test
The continuous reactor performance is described based on the experimental steps of reactor stabilization followed by FeNPs nanoparticle evaluation. Figure 2 illustrates the overall process of continuous operation. During the initial experimental period, the reactor was operated for 24 h to promote the adaptation of the microbiota. After 7 days of continuous operation, H₂ productivity reached 0.7 ± 0.1 NL H₂/L-d, with an average hydrogen content of 58.2 ± 3.7%. In addition to H2, CO₂ was present in the gas composition, while other gases, such as H2S and CH4, were not detected. After reaching a pseudo-steady state, the HRT condition of the reactor was changed to 12 h. The reactor responded positively to this change, reaching a maximum productivity of 0.9 ± 0.1 NL H2/L-d after 2 days; however, a steep decline in H2 production was observed until the complete process collapsed. To promote the growth and activity of acidogenic communities in the reactor, a batch recovery period with lactose (as the sole carbon source) was performed for three days. After this period, the continuous operation mode was restarted, still feeding with lactose as the sole carbon and energy source, with an HRT of 12 h. During this phase, the reactor reached a maximum productivity of 5.6 NL/L-d, indicating the presence of active H2-producing communities. After two days of reactor recovery, there was a sudden drop in H2 productivity associated with the replacement of the lactose-based growth medium with RS hydrolysates and the shortening of HRT to 18 h. It is important to note that lignocellulosic material subjected to acid pretreatment typically generates a significant amount of inhibitors, including furfural, HMF, and other phenolic compounds. Furfural concentrations can range from 0.03 to 8.23 g/L and HMF concentrations from 0.09 to 1.59 g/L [32], often exceeding the inhibition threshold for DF processes, which is approximately 1 g/L [32]. In this context, several techniques have been developed to remove inhibitors prior to substrate utilization in DF systems [33]. Among these methods, over-liming involves the precipitation of inhibitors with Ca(OH)2. Enhanced pretreatment efficacy is achieved through a combination of high pH and temperature over a prolonged period of time. This approach has been recognized as a promising detoxification method for dilute sulfuric acid pretreated lignocellulosic biomass hydrolysate [34]. In addition, over-liming has been used to remove inhibitors from sulfuric acid hydrolysates derived from RS [28]. Based on these considerations, an over-liming treatment was applied to avoid reactor inhibition.
3.2.2. FeNPs Nanoparticles Evaluation
After applying the overliming pretreatment, continuous operation was resumed to assess FeNPs performance. Initially, the reactor was operated in batch mode for two days. Following this, the system transitioned to continuous operation with an HRT of 6 h. The reactor responded favorably, achieving peak H2 productivity of approximately 2.6 NL H2/L-d and stabilizing at an average productivity of 1.7 ± 0.2 NL H2/L-d, with the H2 content in the biogas at 50.7% ± 1.4 (Figure 2 and Table 3). After a two-day period without nanoparticle addition (approximately six steady-state cycles), the evaluation of FeNPs (magnetite) commenced with the introduction of 50 ppm directly into the feed tank. Following the nanoparticle addition, a significant increase in H2 productivity was observed, with the system reaching a steady state of 2.4 ± 0.2 NL H2/L-d with an associated H2 content of 51.6% ± 0.2 (Figure 2). This represented a productivity increase of approximately 41.2%. After six cycles of stable operation, the FeNP concentration was increased to 100 ppm. Initially, a rapid increase in productivity was observed, followed by a brief decrease during the first HRT (6 h). However, productivity soon increased again, stabilizing at 2.6 ± 0.3 NL H2/L-d, which corresponds to an approximate 53% increase compared to the period without nanoparticle addition. During this period, a slight decrease in H2 content was noted, with the value reaching 50.5% ± 0.3 (Table 3). Following six stable cycles, the FeNP concentration was further increased to 200 ppm. This increase led to an immediate drop in H2 productivity, returning to nearly the initial performance observed without nanoparticles at 1.6 ± 0.2 NL H2/L-d. The H2 content remained steady at 50.0% ± 0.5.
Although previous studies have often identified 200 ppm as the optimal Fe nanoparticle concentration, with some systems showing tolerance up to 700 ppm before experiencing inhibition [18,35], the results of this study suggest that inhibition began to occur at magenetite nanoparticle concentrations above 100 ppm. The surface of FeNPs can enhance the activity of ferredoxin oxidoreductase by accelerating electron transfer from NADPH to [Fe–Fe] hydrogenases, thereby boosting H2 production [36,37]. Moreover, nanoparticles possess an electron affinity that facilitates the reduction of protons (H+) to H2, effectively acting as an electron sink [38]. However, at high concentrations, nanoparticles can induce toxicity and generate reactive oxygen species, which negatively impact microbial growth [39]. As a result, while a FeNP concentration of 200 ppm initially appeared promising for enhancing H2 productivity, it ultimately led to a significant reduction in productivity compared to the 50 and 100 ppm concentrations. This behavior can be directly attributed to the specific system and operational conditions. In continuous operation, the constant supply of nanoparticles can lead to a chronic effect that exceeds the microbial utilization rates. In addition, the magnetic properties of FeNPs can cause them to accumulate in the magnetically stirred reactor, thereby increasing their concentration in the system.
The results obtained in this study align with those reported in the literature. For example, Reddy et al. [35] investigated the use of acid hydrolysates of sugarcane bagasse for fermentative H2 production under mesophilic conditions at various pH levels. They found that the addition of Fe2+ nanoparticles at a concentration of 200 ppm increased H2 productivity by nearly 32%. On the other hand, Taherdanak et al. [40] observed a maximum increase in H2 productivity of nearly 43% when Fe0 nanoparticles (25 ppm) were added in batch experiments using glucose under mesophilic conditions. A more comparable study is that of Tawfik et al. [16], who evaluated the effect of adding FeNPs (200 ppm) in a continuous system using RS alkali hydrolysates under various HRTs of 48, 36, 24, 18, 12, and 8 h. Tawfik et al. [16] found a significant impact of nanoparticle addition across all HRTs, with the maximum increase in H2 productivity of 42.1% observed at an HRT of 24 h compared to the control without nanoparticles.
3.3. Metabolic By-Products
Soluble metabolites identified in the effluent of the DF reactor across all operational stages included lactate, formate, acetate, propionate, and butyrate (Figure 3). Regardless of the nanoparticle concentrations tested (0, 50, 100, and 200 ppm), the predominant metabolites produced were acetate, butyrate, and propionate, as shown in Figure 3. Notably, a consistent lactate concentration, averaging around 1.0 g/L, was observed throughout all processes, independent of the nanoparticle condition. This consistency suggests that while nanoparticles may influence the enzymatic pathways associated with H2 production, they do not disrupt or impact the metabolic pathway responsible for lactate production, nor do they affect the enzymes involved in its synthesis.
Conversely, acetate and butyrate have historically been closely associated with H2 production. Notably, the introduction of 50 ppm FeNPs resulted in a 21% increase in butyrate concentration. When the nanoparticle concentration was further increased to 100 ppm, butyrate concentration significantly rose to 2.0 ± 0.1 g/L, which is nearly 20% higher than the previous level and 43% higher than the concentration without nanoparticles. However, at the highest nanoparticle concentration of 200 ppm, there was a decrease in butyrate concentration to 1.8 ± 0.1 g/L.
In contrast, the addition of 50 ppm FeNPs did not result in an increase in acetate concentration, with levels remaining steady at 6.6 ± 0.3 g/L. Moreover, when the nanoparticle concentration was increased to 100 ppm, acetate concentration decreased to 5.5 ± 0.2 g/L, and this downward trend continued with the introduction of 200 ppm nanoparticles. The use of nanoparticles enhances H₂ production primarily through butyrate-type fermentation, with acetate-type fermentation playing a secondary role. Both routes are simplified and described in Equations (1) and (2), respectively. These reactions are exergonic, with standard free energy changes (∆G0) of −206 kJ/mol and −255 kJ/mol, respectively, indicating that both are thermodynamically favorable.
C6H12O6 + 2H2O → 2CH3COOH + 2CO2 + 4H2(1)
C6H12O6 → CH3CH2CH2COOH + 2CO2 + 2H2(2)
In these pathways, glucose is initially broken down into pyruvate, which is then oxidized to acetyl-CoA via the reduction of ferredoxin by the enzyme pyruvate-ferredoxin oxidoreductase. The reduced ferredoxin is subsequently oxidized by the hydrogenase enzyme to produce oxidized ferredoxin and H₂. Additionally, excess NADH produced during glycolysis can be oxidized by NADH-[Fe-Fe] hydrogenase to generate more H₂. Acetyl-CoA is then converted into acetate, butyrate, or ethanol, depending on environmental conditions and the specific microorganisms present [14]. However, the pH conditions in this study favored the butyrate pathway, which may explain why acetate formation does not directly correlate with increased H2 productivity. Moreover, studies like those by Taherdanak et al. [40] suggest that Fe nanoparticles can enhance butyrate production while inhibiting ethanol production, thereby increasing H2 productivity. The decrease in propionate concentration with the addition of FeNPs, as shown in Figure 3, also contributed to the increase in H₂ production.
3.4. Microbial Analysis
To identify the microorganisms involved in the DF process, a comparison of relative abundance across different process conditions was conducted (Figure 4). The analysis at the genus level revealed that Bacteroides consistently dominated the microbial community, with relative abundance varying slightly depending on the nanoparticle conditions, ranging from 55% to 65%. Bacteroidetes, the phylum to which Bacteroides belong, are known as proteolytic bacteria and are likely involved in the degradation of various proteins. These proteolytic microorganisms have the capability to metabolize amino acids, leading to the production of volatile fatty acids (VFAs) such as acetate, propionate, succinate, and ammonia (NH₃) [41].
Following Bacteroides, the subdominant genera identified include Clostridium, Lachno-clostridium, Veillonella, and Robinsonella. Initially, the addition of 50 ppm FeNPs resulted in a decrease in the relative abundance of Clostridium from 10% to 6%. However, as the NPs concentration was increased to 100 ppm and 200 ppm, the relative abundance of Clostridium rose to 19% under both conditions. Clostridium is well-known as a classic H2-producing genus in DF [42]. While Clostridium can utilize simple sugars for H2 production, previous studies have demonstrated its ability to produce H2 from more complex substrates as well [43]. The molecular analysis used in this study did not allow for a correlation between H₂ production and the relative abundance of Clostridium, as it does not reflect bacterial activity. When the NPs concentration was increased to 100 ppm and 200 ppm, the relative abundance of Clostridium increased; however, this did not correspond to an enhancement in H2 productivity. In fact, H2 productivity decreased at FeNPs 200 ppm condition. Despite the overall increase in Clostridium species with higher nanoparticle concentrations, there was a notable decline in species such as Clostridium butyricum, which are strongly associated with H2 production.
The subdominant genera identified in the study are not strictly associated with H2 production. For example, Lachnoclostridium, which belongs to the family Lachnospiraceae, is primarily linked to the production of VFAs, including acetic, propionic, and butyric acids [44]. Many species within the Lachnoclostridium genus are strict anaerobes that ferment carbohydrates, leading to the synthesis of these organic acids. Notably, certain strains of Lachnoclostridium are particularly known for their ability to produce butyric acid. Similarly, Robinsoniella has been shown to grow on a variety of polysaccharides and monosaccharides, including amygdalin, arabinose, cellobiose, fructose, glucose, maltose, lactose, raffinose, starch, trehalose, xylan, and xylose [45]. Finally, Veillonella is associated with the production of acetic and propionic acids as major end products [46]. Although these genera are not directly associated with H2 production, their significant reduction under the highest NPs concentration condition (200 ppm) could be related to the observed decrease in H2 production. The synergistic action and mutualistic relationships among mixed bacterial communities play a crucial role in the fermentation process for H2 production.
3.5. Preliminary Energy and Economic Analysis of RS Valorization through DF
This section provides a preliminary analysis of the economic and energy aspects related to the proposed valorization processes. As previously noted, the large volume of RS generated by agro-industrial activities presents significant management challenges. In this context, the valorization scenarios proposed in this study aim to improve the cost–benefit ratio of RS utilization. For example, under conditions of 150 ppm magnetite nanoparticle supplementation, the maximum H2 production achieved was 9.3 m3 per ton of dry RS. Given the current average price of green H2 at EUR 7.5/kg [47], the DF process could generate EUR 6.3 per ton of dry RS. Furthermore, considering the cost of DF, estimated at approximately EUR 3/kg H₂ [48], the net value would be EUR 3.3 per ton of dry RS, 1.5 times higher than the value obtained without nanoparticle supplementation.
However, the use of FeNPs introduces additional economic costs. While FeNPs have demonstrated their potential to enhance fermentative H2 production, several key factors must be addressed to ensure their economic sustainability. These include optimizing nanoparticle concentrations, avoiding reliance on commercially available powders, selecting appropriate nanoparticle types, sizes, and shapes, and improving distribution efficiency. Additionally, the introduction of organic or non-metallic nanoparticles that are biodegradable and non-toxic is essential to minimize environmental impact and enhance the overall feasibility of nanoparticle use. Lastly, strategies for the recovery, reuse, or potential recirculation of FeNPs must be considered to reduce their economic burden.
In terms of energy recovery, the higher H2 production achieved with 150 ppm FeNP supplementation resulted in an energy yield of 118 kJ/kg of dry RS, representing a roughly 50% increase compared to conditions without FeNP addition. Despite these observed benefits, further strategies, such as coupling DF with anaerobic digestion systems, should be explored to improve the overall feasibility and sustainability of the process.
Moreover, future economic analyses must account for the broader environmental benefits associated with valorizing RS through DF, particularly when coupled with other biotechnologies within a biorefinery framework. This approach not only enhances the economic value of RS but also contributes to mitigating its environmental impacts by offering sustainable management solutions for this abundant agricultural residue.
4. Conclusions
The performance of a continuous H2 production system using RS hydrolysates was evaluated by comparing conditions without FeNPs in addition to three different magnetite nanoparticle concentrations. The highest H2 productivity, 2.6 ± 0.3 NL H2/L-d, was achieved with the addition of 100 ppm of FeNPs, representing a 53% increase compared to the non-nanoparticle condition. In contrast, increasing the FeNP concentration to 200 ppm led to a decrease in H2 productivity to 1.6 ± 0.2 NL H2/L-d. The primary metabolites produced were acetate, butyrate, and propionate, with the maximum butyrate concentration of 2.0 ± 0.1 g/L observed at the 100 ppm FeNPs concentration. Microbial community analysis showed the dominance of Bacteroides and Clostridium, with the latter being a key genus associated with H2 production, particularly favored at the 100 ppm nanoparticle concentration. While FeNPs have shown potential for enhancing H2 productivity in a DF continuous system using RS hydrolysates, further research is needed to explore their effects in combination with other operational parameters, such as extended operation times, varying HRTs, and different pH conditions. Additionally, a techno-economic analysis is essential to assess the viability of this process, including the development of strategies for nanoparticle recovery.
Conceptualization, O.G.-D.; methodology, C.M.-F., E.L.-B. and O.G.-D.; formal analysis, J.P.-B.; investigation, J.P.-B. and C.M.-F.; resources, R.M.; writing—original draft, J.P.-B.; writing—review and editing, R.M., L.V.-E., R.M.-Y., E.L.-B., R.C.-M. and O.G.-D.; visualization, J.P.-B. and L.V.-E.; supervision, R.M., R.M.-Y., E.L.-B., R.C.-M. and O.G.-D.; project administration, O.G.-D.; funding acquisition, E.L.-B. and O.G.-D. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 2. Overall continuous operation for hydrogen production. Hydrogen productivity (●), hydrogen content in the evolved off-gas (■).
Enlarge this image.Figure 3. Organic acid profile during FeNPs evaluation of fermentative H2 production from RS hydrolysates pretreated by dilute acid-assisted thermal pretreatment.
Enlarge this image.Figure 4. Structure of the microbial community at the genus level in the acidogenic broth under different conditions. The category “others” includes genera with a relative abundance of less than 1%.
Physicochemical composition of raw RS.
| Parameter (% w/w on a Dry Basis) | RS |
|---|---|
| Moisture | 7.2 ± 0.8 |
| Ash | 12.7 ± 0.9 |
| Total solids | 92.3 ± 0.8 |
| Acid-insoluble lignin | 20.5 ± 0.2 |
| Acid-soluble lignin | 4.5 ± 0.1 |
| Total lignin | 25.0 ± 0.2 |
| Cellulose | 25.4 ± 0.2 |
| Hemicellulose | 7.8 ± 0.1 |
Physicochemical characterization of hydrolysates obtained from RS pretreated by dilute acid-assisted thermal pretreatment.
| Parameter | RS Hydrolysate |
|---|---|
| TCOD (g/L) | 33.7 ± 1.3 |
| SCOD (g/L) | 28.5 ± 0.9 |
| TOC (g/L) | 8.9 ± 0.3 |
| TN (g/L) | 0.4 ± 0.0 |
| Total solids (g/L) | 53.7 ± 2.3 |
| Volatile solids (g/L) | 47.5 ± 3.1 |
| Reducing sugars (g/L) | 22.1 ± 0.6 |
| * COD yield (%) | 48.2 ± 2.3 |
TCOD: Total chemical oxygen demand. SCOD: Soluble chemical oxygen demand. TOC: Total organic carbon. TN: Total nitrogen. * Ratio of TCOD in hydrolysates to the total solid concentration used.
Summary of the process performance indicators during the continuous fermentative process for hydrogen production from RS hydrolysates with FeNPs addition.
| Condition | Average H2 Productivity (NL H2/L-d) | Average H2 Content (%) |
|---|---|---|
| HRT 6 (RS) | 1.7 ± 0.2 a | 50.7 ± 1.4 |
| HRT 6 (RS) NPs 50 ppm | 2.4 ± 0.2 b | 51.6 ± 0.2 |
| HRT 6 (RS) NPs 100 ppm | 2.6 ± 0.3 c | 50.5 ± 0.3 |
| HRT 6 (RS) NPs 200 ppm | 1.6 ± 0.2 a | 50.0 ± 0.5 |
Note: Within a specific condition, different letters in a column indicate values that are significantly different at the 95% confidence level.
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Details
; Vargas-Estrada, Laura 2 ; Maya-Yescas, Rafael 3
; León-Becerril, Elizabeth 4
; Castro-Muñoz, Roberto 5
; García-Depraect, Octavio 2
1 Department of Environmental Technology, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A. C. Av. Normalistas 800, Colinas de la Normal, Guadalajara 44270, Jalisco, Mexico;
2 Institute of Sustainable Processes, University of Valladolid, Dr. Mergelina s/n., 47011 Valladolid, Spain; Department of Chemical Engineering and Environmental Technology, University of Valladolid, Dr. Mergelina s/n., 47011 Valladolid, Spain
3 Faculty of Chemical Engineering, Universidad Michoacana de San Nicolás de Hidalgo, Ciudad Universitaria, Morelia 58030, Michoacán de Ocampo, Mexico;
4 Department of Environmental Technology, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco, A. C. Av. Normalistas 800, Colinas de la Normal, Guadalajara 44270, Jalisco, Mexico;
5 Faculty of Civil and Environmental Engineering, Department of Sanitary Engineering, Gdansk University of Technology, 11/12 Narutowicza St., 80-233 Gdansk, Poland;